Biology subjects

Dzafic, I.

Publications and source records attributed to Dzafic, I..

4 recordsLinked to original sources

bioRxiv2021

Anxiety attenuates learning advantages conferred by statistical stability and induces loss of volatility-attuning in brain activity

Anxiety can alter an individuals perception of their external sensory environment. Previous studies suggest that anxiety can increase the magnitude of neural responses to unexpected (or surprising) stimuli. Additionally, surprise responses are reported to be boosted during stable compared to volatile environments. Few studies, however, have examined how learning is impacted by both threat and volatility. To investigate these effects, we used threat-of-shock to transiently increase subjective anxiety in healthy adults during an auditory oddball task, in which the regularity could be stable or volatile, while undergoing functional Magnetic Resonance Imaging (fMRI) scanning. We then used Bayesian Model Selection (BMS) mapping to pinpoint the brain areas where different models of anxiety displayed the highest evidence. Behaviourally, we found that threat-of-shock eliminated the accuracy advantage conferred by environmental stability over volatility in the task at hand. Neurally, we found that threat-of-shock led to both attenuation and loss of volatility-attuning of neural activity evoked by surprising sounds across most subcortical and limbic brain regions including the thalamus, basal ganglia, claustrum, insula, anterior cingulate, hippocampal gyrus and also the superior temporal gyrus. Conversely, within two small clusters in the left medial frontal gyrus and extrastriate area, threat-of-shock boosted the neural activity (relative to the safe and volatile condition) to the levels observed during the safe and stable condition, while also inducing a loss of volatility-attuning. Taken together, our findings suggest that threat eliminates the learning advantage conferred by statistical stability compared to volatility. Thus, we propose that anxiety disrupts behavioural adaptation to environmental statistics, and that multiple subcortical and limbic regions are implicated in this process.

neuroscience↗

bioRxiv2019

Aberrant connectivity in auditory precision encoding in schizophrenia spectrum disorder and across the continuum of psychotic-like experiences

BackgroundThe ability to generate a precise internal model of statistical regularities is impaired in schizophrenia. Predictive coding accounts of schizophrenia suggest that psychotic symptoms may be explained by a failure to build precise beliefs or a model of the world. The precision of this model may vary with context. For example, in a noisy environment the model will be more imprecise compared to a model built in an environment with lower noise. However compelling, this idea has not yet been empirically studied in schizophrenia. Methods: In this study, 62 participants engaged in a stochastic mismatch negativity paradigm with high and low precision. We included inpatients with a schizophrenia spectrum disorder (N=20), inpatients with a psychiatric disorder but without psychosis (N=20), and healthy controls (N=22), with comparable sex ratio and age distribution. Bayesian mapping and dynamic causal modelling were employed to investigate the underlying microcircuitry of precision encoding of auditory stimuli. Results: We found strong evidence (exceedance p > 0.99) for differences in the underlying connectivity associated with precision encoding between the three groups as well as on the continuum of psychotic-like experiences assessed across all participants. Critically, we show changes in interhemispheric connectivity between the two inpatient groups, with some connections further aligning on the continuum of psychotic-like experiences. Conclusions: While our results suggest continuity in backward connectivity alterations with psychotic-like experiences regardless of diagnosis, they also point to specificity for the schizophrenia spectrum disorder group in interhemispheric connectivity alterations.

neuroscience↗

bioRxiv2018

Predicting Individual Psychotic Experiences on a Continuum Using Machine Learning

Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict subclinical psychotic-like experiences on a continuum between these two extremes in otherwise healthy people. We applied two different approaches to an auditory oddball regularity learning task obtained from N = 73 participants: O_LIA feature extraction and selection routine incorporating behavioural measures, event-related potential components and effective connectivity parameters; C_LIO_LIRegularisation of spatiotemporal maps of event-related potentials. C_LI Using the latter approach, optimal performance was achieved using the response to frequent, predictable sounds. Features within the P50 and P200 time windows had the greatest contribution toward lower Prodromal Questionnaire (PQ) scores and the N100 time window contributed most to higher PQ scores. As a proof-of-concept, these findings demonstrate that EEG data alone are predictive of individual psychotic-like experiences in healthy people. Our findings are in keeping with the mounting evidence for altered sensory responses in schizophrenia, as well as the notion that psychosis may exist on a continuum expanding into the non-clinical population.

neuroscience↗

bioRxiv2018

Reduced top-down connectivity as an underlying mechanism for psychotic experiences in healthy people

Our perceptions result from the brains ability to make inferences, or predictive models, of sensory information. Recently, it has been proposed that psychotic traits may be linked to impaired predictive processes. Here, we examine the brain dynamics underlying sensory learning and inference in stable and volatile environments, in a population of healthy individuals (N=75) with a range of psychotic-like experiences. We measured prediction error responses to sound sequences with electroencephalography, gauged sensory inference explicitly by behaviourally recording sensory regularity learning errors, and used dynamic causal modelling to tap into the underlying neural circuitry. We discuss the findings that were robust to replication across the two experiments (N=31 and N=44 for the discovery and the validation datasets, respectively). First, we found that during stable conditions, participants demonstrated a stronger predictive model, reflected in a larger prediction error response to unexpected sounds, and decreased regularity learning errors. Moreover, individuals with attenuated prediction errors in stable conditions were found to make greater incorrect predictions about sensory information. Critically, we show that greater errors in sensory learning and inference are related to increased psychotic-like experiences. These findings link neurophysiology to behaviour during sensory learning and prediction formation, as well as providing further evidence for the idea of a continuum of psychosis in the healthy, non-clinical population. Significance StatementWhilst perceiving the world, we make inferences by learning the regularities present in the sensory environment. It has been argued that psychosis may emerge due to a failure to learn sensory regularities, resulting in an impaired representation of the world. Recently it has been proposed that psychosis exists on a continuum; however, there is conflicting evidence on whether sensory learning deficits align on the non-clinical end of the psychosis continuum. We found that sensory learning is associated with brain prediction errors, and critically, it is impaired in healthy people who report more psychotic-like experiences. We replicated these findings in an independent sample, demonstrating strengthened credibility to support that the continuum of psychosis extends into the non-clinical population.

neuroscience↗